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30,411 Article Results

Analysis of mobile banking adoption in Ghana: do education levels differ?

10.11591/ijaas.v14.i3.pp828-837
Isaac Asampana , Lawrence Kwami Aziale , Henry Matey Akwetey , Hannah Ayaba Tanye
This study investigates the role of educational attainment in mobile banking (m-banking) adoption in Ghana, leveraging data from 598 respondents through a multi-group analysis. By integrating the technology acceptance model (TAM) and the theory of planned behavior (TPB) into a structural equation modelling framework, the research examines key factors such as subjective norms, perceived usefulness, ease of use, trust, and self-efficacy. Results reveal significant differences in adoption behaviors between lower- and higher-educated users. Subjective norms strongly influence higher-educated individuals, while perceived ease of use drives adoption among lower-educated users. Perceived usefulness positively affects higher-educated users but has a negative impact on lower-educated respondents. The findings highlight the moderating effect of education level on the adoption process, offering theoretical and practical insights into targeted strategies for enhancing financial inclusion in developing economies. These results underscore the importance of user segmentation in fostering broader acceptance and utilization of m-banking technologies.
Volume: 14
Issue: 3
Page: 828-837
Publish at: 2025-09-01

Optimizing retail systems: using big data and power business intelligence for performance insights

10.11591/ijaas.v14.i3.pp945-954
Huu Dang Quoc , Ha Le Viet
In the rapid development of information technology, using enterprise data to support timely management decisions is crucial in helping businesses operate effectively and improve competitiveness. This study uses Microsoft power business intelligence (MPBI) to analyze data in retail systems, allowing managers to grasp the business situation in real time, track advanced sales, optimize inventory control, and analyze customer behavior and supply chain visibility. From the data generated by the business, the study uses the streaming extract transform load (ETL) model to support real-time data aggregation, then converts to the MPBI data visualization system to convert data into visual charts, helping businesses easily monitor, track, analyze, and make decisions to promote business activities. The study proposes a data structure to organize retail information storage. It proposes a system of calculation formulas and data synthesis, making integrate and convert tabular data into visual charts. Through analysis of real data from the LH83 retail system, the study shows the feasibility of implementing a data visualization system and the difficulties encountered when businesses want to deploy this model.
Volume: 14
Issue: 3
Page: 945-954
Publish at: 2025-09-01

Fuzzy logic controller-based protection of direct current bus using solid-state direct current breaker

10.11591/ijaas.v14.i3.pp859-868
Eswaraiah Giddalur , Askani Jaya Laxmi
Low-voltage direct current (LVDC) microgrids are increasingly utilized due to their efficiency and compatibility with distributed energy resources (DERs) and direct current (DC) loads, eliminating the need for multiple energy conversions. However, the protection of LVDC systems presents significant challenges, including high fault currents and the vulnerability of electronic devices. Traditional electromechanical circuit breakers are inadequate due to their slow response times. This work presents a protection approach for the DC bus in LVDC microgrids that combines a fuzzy logic controller (FLC) with a solid-state circuit breaker (SSCB). The FLC is designed to detect and respond to faults rapidly by processing input variables such as current magnitude and rate of change of current. The FLC controls the SSCB, which interrupts fault currents quickly and reliably. The proposed system demonstrates optimized fault-clearing times within milliseconds, significantly enhancing the protection and reliability of LVDC microgrids. This novel solution protects critical electronic components while also ensuring the microgrid's operational integrity. The FLC approach is utilized for optimizing fault-clearing duration within milliseconds.
Volume: 14
Issue: 3
Page: 859-868
Publish at: 2025-09-01

Sulphur corrosion in transformer insulating oils: its effects, detection methods, and mitigation strategies

10.11591/ijaas.v14.i3.pp784-792
Nur Izyan Husnina Zulkefli , Sharin Ab Ghani , Mohd Shahril Ahmad Khiar , Imran Sutan Chairul , Nor Hidayah Rahim , Nur Farhana Mohd Azlan
Oil-immersed transformers are subjected to electrical, thermal, and mechanical stresses over time, which inevitably affect the insulating oil and paper insulation. The presence of sulphur corrosion also degrades the insulating oil and paper insulation. Sulphur corrosion in insulating oils has been a prevalent problem for many years, as it culminates in the failure of oil-immersed transformers. The longevity of oil-immersed transformers is dependent on the integrity of the insulating oil and paper insulation, which can deteriorate owing to sulphur corrosion. The occurrence and accumulation of copper sulphide (Cu2S) can result in transformer malfunctions, which is a significant issue for transformer manufacturers and operators. This paper provides a concise overview of the effects of sulphur corrosion, its detection methods, as well as its mitigation strategies. It is believed that this paper will enhance the understanding of sulphur corrosion in insulating oils, provide the best practices for sulphur corrosion management, and serve as guidance on enhancing transformer reliability and performance.
Volume: 14
Issue: 3
Page: 784-792
Publish at: 2025-09-01

Therapeutic potential of alpha-linolenic acid from Sacha Inchi oil in cervical cancer: an in vitro study on HeLa cells

10.11591/ijaas.v14.i3.pp966-974
Adi Permadi , Mutiara Wilson Putri , Muhammad Ali Akbar
This study investigated the potential of alpha-linolenic acid (ALA) from Sacha Inchi oil as a therapeutic agent for cervical cancer through an in vitro study on HeLa cells. Cervical cancer is one of the most common types of cancer in women, which is often caused by human papillomavirus (HPV) infection. Although chemotherapy therapy is one of the main methods in cancer treatment, this approach often causes side effects and drug resistance. ALA, which is one of the main components of Sacha Inchi oil, is known to have antioxidant and anti-cancer activities. In this study, Sacha Inchi oil was analyzed using liquid chromatography-high resolution mass spectrometry (LC-HRMS) for identification of its active components. Cytotoxic assays were performed using the MTT method on HeLa cells, which showed that ALA significantly inhibited cancer cell viability at low concentrations, with low IC50 values compared to the positive control compound cisplatin. These results suggest that ALA has potential as an effective anti-cancer agent against cervical cancer cells. This study concludes that ALA from Sacha Inchi oil can be a strong candidate in the development of safer and more effective cervical cancer therapy.
Volume: 14
Issue: 3
Page: 966-974
Publish at: 2025-09-01

Comprehensive structured analysis of machine learning in safety models

10.11591/ijaas.v14.i3.pp627-638
Mohd Shukri Abdul Wahab , Syed Tarmizi Syed Shazali , Noor Hisyam Noor Mohamed , Abdul Rani Achmed Abdullah
Machine learning (ML) integration into various industries has revolutionized operations recently, enhancing efficiency and predictive capabilities. However, the rapid adoption of ML models also presents significant safety concerns that are highly demanded. To achieve this, scholarly articles from reputable databases such as Scopus and Web of Science (WoS) focus on studies published between 2022 and 2024, which were extensively searched. The study's flow is based on the PRISMA framework. The database found (n=40) that the final primary data was analyzed. The findings were divided into three themes: i) safety and risk management, ii) ML and artificial intelligence (AI) applications in safety, and iii) smart technology for safety. The conclusion highlights the need for continuous monitoring and updating of the safety protocols to keep in step with the growing ML landscape. This review contributes to the understanding of ML safety. It offers global lessons that can guide future research and policy-making efforts to ensure ML technologies' safe and ethical use.
Volume: 14
Issue: 3
Page: 627-638
Publish at: 2025-09-01

Numerical study of non-linear twisted blades for tidal turbines improvement

10.11591/ijaas.v14.i3.pp894-906
Nu Rhahida Arini , Philips Ade Putera Atmojo , Deni Saputra , Dendy Satrio
Despite the growing demand for renewable energy, the utilization of tidal energy remains underdeveloped due to efficiency limitations in turbine design. Addressing this gap, this study investigates the performance of horizontal-axis tidal turbines (HATT) by comparing two foil designs, National Advisory Committee for Aeronautics (NACA) 2415 and OptA, to optimize energy extraction efficiency. The research employs computational fluid dynamics (CFD) simulations using OpenFOAM to evaluate the effects of foil modifications and non-linear twist distributions on turbine performance across varying tip speed ratios (TSR). The results indicate that the OptA foil significantly improves turbine performance, achieving a 41.4% increase in torque and a 40.2% increase in power coefficient (CP) at TSR 5, which was identified as the optimal operating condition. The OptA foil enhances velocity distribution, reduces flow separation, and improves vortex behavior, leading to greater efficiency and stability. These findings confirm that foil selection and blade design modifications play a critical role in HATT optimization.
Volume: 14
Issue: 3
Page: 894-906
Publish at: 2025-09-01

Searchable encryption based on a chaotic system and AES algorithm

10.11591/ijaas.v14.i3.pp975-984
Fairouz Sherali , Falah Sarhan
Cloud computing provides on-demand access to computing resources, such as storage and processing power. This technology allows businesses to scale efficiently while reducing infrastructure costs. However, protecting the security and privacy of data has grown to be a top priority. This is where enhancing cloud security with searchable encryption (SE) is crucial. SE effectively secures users’ sensitive data while preserving searchability on the cloud server side. It enables the cloud server to search via encrypted data without disclosing information in plaintext data. SE uses different encryption methods to encrypt data before uploading it to servers. The advanced encryption standard (AES) is a common algorithm for encrypting this data. In this paper, a novel SE method has been presented. The technique exploits the properties of the chaotic map to generate an AES key, which makes the AES algorithm more secure for encrypting the searchable index and uploaded files. We implement and test our method with real data from files. The experimental results show that the proposed method can significantly satisfy a higher level of security as compared to other schemes.
Volume: 14
Issue: 3
Page: 975-984
Publish at: 2025-09-01

Bibliometric visualization of metal-air battery research trends

10.11591/ijpeds.v16.i3.pp1865-1880
Satria Pinandita , Rustam Asnawi , Mochamad Syamsiro
Metal-air batteries are rechargeable secondary batteries with high energy density, typically using carbon electrodes. However, carbon waste poses environmental risks. Fly ash, a byproduct of coal combustion, offers a sustainable alternative due to its high electrical conductivity. This study analyzes research trends on metal-air batteries and fly ash from 2019 to 2023 using bibliometric visualization of Scopus-indexed publications. The keyword search was refined from 'Battery' to 'Air Battery' and, finally, 'Air Battery' with 'Fly Ash,' yielding 60 relevant articles. Using the VOSviewer, research patterns, key focus areas, and collaboration networks were identified. The results indicate a 14.87% increase in publications from 2019 to 2023, with significant growth from 2019 to 2021 before declining after 2022. This fluctuation suggests shift in research interests toward other battery technologies. Fly ash demonstrates potential as a carbon substitute for air batteries, promoting sustainability. However, further research is needed to optimize its application and address technical challenges. Bibliometric visualization highlights a growing interest in fly ash for environmentally friendly battery development due to its abundance and sustainability.
Volume: 16
Issue: 3
Page: 1865-1880
Publish at: 2025-09-01

Haystack-based Facebook’s data storage architecture: store, directory, and cache

10.11591/ijaas.v14.i3.pp671-681
Tole Sutikno , Ahmad Heryanto , Laksana Talenta Ahmad
Haystack is Facebook's unique way of managing large amounts of user-generated content like photos. The architecture prioritizes performance, reliability, and scalability to overcome network-attached storage system bottlenecks. Haystack speeds data access and ensures data integrity during hardware failures by using physical and logical volumes. This study examines the architecture of Facebook's Haystack data storage system and its effects on scalability and efficiency in handling large photo data. According to the study, the store, directory, and cache functions work together to reduce input/output (I/O) operations and improve metadata processing, which traditional network-attached storage systems cannot do. Haystack manages massive photo data storage and retrieval, solving network-attached storage (NAS) limitations. It balances throughput and latency by minimizing disk operations and optimizing metadata processing. Each store, directory, and cache contribute to this ecosystem. The Haystack architecture reduces disk operations and metadata processing bottlenecks with distributed caching. A cache allows instant access to frequently requested images and balances read and write operations across the system. We should study advanced storage system architectures based on Facebook's Haystack architecture. This could involve investigating faster metadata processing algorithms, using artificial intelligence (AI) to improve fault detection and repair systems, and assessing the economic impact of distributed caches.
Volume: 14
Issue: 3
Page: 671-681
Publish at: 2025-09-01

An innovative approach to Raga pattern identification

10.11591/ijeecs.v39.i3.pp1865-1876
Sudipta Chakrabarty , Prativa Rai , Md Ruhul Islam , Hiren Kumar Deva Sarma
Raga is a fundamental element of Indian classical music (ICM), crucial for identifying the unique characteristics of a given song. Recognizing the embedded Raga allows for various applications, including music therapy, and leveraging the therapeutic effects of different Ragas. The use of mathematical techniques such as fast fourier transform (FFT) and fundamental frequency measurement (FFM) in calculating note values has proven effective for Raga pattern recognition. Both methods yield nearly identical results, facilitating accurate identification of Ragas. Once identified, these Ragas can be used for specific therapeutic purposes, harnessing their healing potential.
Volume: 39
Issue: 3
Page: 1865-1876
Publish at: 2025-09-01

A method classifying the domestic tourist destination base similarity measuring

10.11591/ijaas.v14.i3.pp740-750
Nguyen Thi Hoi , Tran Thi Nhung , Bui Quang Truong , Nguyen Quang Trung
The classification problem is crucial in business, providing an effective method for supporting search activities in areas such as e-commerce, education, and marketing. This has become especially important in the wake of the COVID-19 pandemic, which has increased the need to promote and stimulate domestic tourism. This research focuses on recommending tourist destinations based on historical search data related to domestic tourism. The study uses techniques like term frequency-inverse document frequency (TF-IDF) weight vector analysis and similarity measures to calculate recommendation scores. Data was collected from various tourism websites, covering destinations across all 63 provinces and cities in Vietnam. Experiments were conducted using three approaches: cosine similarity, the brute force algorithm, and long short-term memory (LSTM) for long-text processing. The results indicate that similarity-based methods produce recommendations that closely match user preferences. For full-sentence queries, the brute force algorithm delivers more accurate results, while LSTM provides faster processing times. These findings offer businesses multiple strategies for improving recommender systems in practical applications.
Volume: 14
Issue: 3
Page: 740-750
Publish at: 2025-09-01

Redesign the layout of the raw material warehouse from randomized storage to class-based storage

10.11591/ijaas.v14.i3.pp773-783
Nur Iftitah , Qurtubi Qurtubi , Danang Setiawan , Vembri Noor Helia
The company has a problem of ineffectiveness in the layout of the raw material warehouse due to the use of storage methods that ignore factors such as the type, dimensions, and condition of the goods. This reduces the optimal function of the warehouse and increases the time to retrieve goods. This research aims to redesign the suitable and practical layout of the raw material warehouse by considering its form and function, as well as filling methodological gaps from previous research. The method used is class-based storage. Based on ABC analysis, the category with the highest value is class C goods, with 73 units. Meanwhile, from the fast, slow, non-moving (FSN) analysis, class F (fast-moving) goods have the highest frequency of movement, with a movement percentage of 63% for 10 units of goods. The warehouse slotting analysis shows an increase in the number of shelves from nine to 15 shelves with five different shelf models and layout changes in raw material warehouses 1 and 2. The class-based storage method results in a more organized layout, efficient movement of goods, and faster picking time to optimize warehouse functions.
Volume: 14
Issue: 3
Page: 773-783
Publish at: 2025-09-01

Test rig development for load test of pipe saddle support

10.11591/ijaas.v14.i3.pp886-893
Muhammad Arif Rayhan , Mohd Shukri Yob , Mohd Juzaila Abd Latif , Ojo Kurdi , Fudhail Abdul Munir
Pipe saddle support is a structure commonly used to support horizontal steel pipe. It prevents direct contact between the pipe and the support. Pipe saddle support can experience displacement due to pipe movement and insufficient stress analysis. Given these concerns, conducting a load test is essential to determine the stress on pipe saddle supports. However, a universal testing machine (UTM) is not suitable for this purpose due to the size limitation. Therefore, this study proposed a test rig setup for the pipe saddle support load test. The test rig consists of a portal frame secured by an underground locking system featuring a strong floor. Additionally, an actual pipe is utilized to replicate actual loading conditions on the pipe saddle support. The applied load is measured using a load cell, with a custom-designed bracket to ensure precise load transfer. Finally, the pipe saddle support specimen is bolted to a base support to maintain stability during the load test. Stress analysis using finite element analysis (FEA) demonstrated that the test rig is suitable for conducting load tests on the specimens with a maximum force of 80 kN. FEA confirmed that the test rig operates within a safety factor of 1.3.
Volume: 14
Issue: 3
Page: 886-893
Publish at: 2025-09-01

Determination of soil salinization by hyperspectral remote sensing in the Shirvan Plain

10.11591/ijaas.v14.i3.pp662-670
Sahib Shukurov Khudaverdi , Aygun Ismayilova Azer , Ramil Sadigov Ali , Maya Karimova Javanshir , Turkan Hasanova Allahverdi , Gunel Asgarova Farhad
The determination of soil salinization in the Shirvan Plain, considered the main agricultural zone of Azerbaijan, negatively affects the productivity of agricultural crops. Based on 10 m Sentinel-2 images on Google Earth Engine platforms and by examining SI1, green-red band normalized difference vegetation index (GRNDVI), green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), and difference vegetation index of the environment (DVI), four remote sensing salinity monitoring index models, S1DI1, S1DI2, S1DI3, and S1DI4, were constructed to extract soil salinity information in the Shirvan Plain in combination with the measured electrical conductivity. The results show that the overall classification accuracy of S1DI1 (SI1-GRNDVI), S1DI2 (SI1-GNDVI), S1DI3 (SI1-NDVI), and S1DI4 (SI1-DVI) models for salinity monitoring are 82.35%, 83.10%, 81.96%, and 79.25%, respectively.
Volume: 14
Issue: 3
Page: 662-670
Publish at: 2025-09-01
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